Development of an Information System for Personnel Management in Schools under the Surat Thani Primary Educational Service Area Office
Bibliographic record
Abstract
This research aimed to develop and evaluate an Information System for Personnel Management (ISPM) tailored for schools under the Surat Thani Primary Educational Service Area Office. Employing a research and development (R&D) methodology, the study was conducted in three phases: (1) a needs analysis through in-depth interviews with 12 school administrators to identify problems and system requirements; (2) system development using the System Development Life Cycle (SDLC) model, followed by expert validation from seven IT professionals; and (3) implementation and effectiveness evaluation with 17 school personnel over a one-month trial. Results from Phase 1 indicated fragmented and inefficient practices, with most schools relying on manual or semi-digital tools. In Phase 2, the developed system was rated highly appropriate across dimensions such as data security (M = 4.95, SD = 0.53) and usability (M = 4.83, SD = 0.38). Phase 3 findings confirmed the system’s effectiveness, with an overall satisfaction rating of M = 4.82 (SD = 0.37). The study contributes to the field by offering a full-cycle model of ISPM development grounded in actual school needs and validated through expert and end-user feedback. Limitations include the restricted trial scope and short duration, suggesting the need for future research on scalability and long-term use. The system provides a practical solution for enhancing personnel data management in Thai schools and potentially across broader educational contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".